Tuesday, May 17, 2011

Since I posted the offensive ratings of the 2500+ returning players based on the % of points they added to their teams, I’ve been asked why I didn’t also calculate the defensive % of points good players take away from their opponents.

The fact that so few defensive stats are kept is cumbersome for someone who lives number crunching professionally.

Ironically, this is the topic of a talk I will be giving to a couple of colleges, “Winning With Political Microtargeting and Sports Sabermatrics.” The fact is that everywhere in my professional work from City Council Campaigns to a couple of successful Presidential campaigns, I have used Microtargeting to calculate every message and delivery method (door-to-door, mail, TV), and how many votes a dollar spent on each will get FOR my candidate (offense) or dissuade people from voting for our opponent (defense). Likewise in basketball we keep offensive stats every time the team scores or fails to score, BUT ironically we only keep stats of 22% of the defensive contributions. To illustrate, below are the different ways a basketball possession can end, and how likely each is to happen per 100 possessions. (Parenthesis mean there is a stat that should be kept, but is not):

Possession ends with

#

Pts

Off credit/blame

Def credit/blame

3pts made

9

27

3PM (split credit if AST)

(PtsAllowed)

2pt made

26

52

FGM (split credit if AST)

(PtsAllowed)

FT made

13

19

FTM

(PtsAllowed)

Steal

9

0

TO

STL

Turnover other than steal

11

0

TO

(TOF)

blocked shot, def reb

3

0

FGA

2/3rd BLK, 1/3 REB

other missed 2, def reb

14

0

FGA

2/3rd (STOP), 1/3 REB

missed 3 pt, def reb

11

0

FGA

2/3rd (STOP), 1/3 REB

missed FT, def reb

4

0

FTA

2/3rd (STOP), 1/3 REB

100 possessions ended (2pts during possession))

100

100

credit/blame for 100%

credit/blame for 22%

(See note at bottom on why shooting percentages look higher than they are at first glance)

Please don't use this as an excuse to comment on politics, but comparing what is tracked is enlightning for me.

When Moneyball came out in 2003, the top Bush campaign guys went crazy over it. They loved the Sabermatrics behind it, and pretty soon they had built the most incredible Microtargeting model ever, calculating that the biggest bang for their campaign dollar would be getting specific people in specific Ohio localities who were predisposed to Bush to vote after sitting out the 2000 campaign. The fact that we don’t keep track of a player’s points allowed (PtsAllowed), forced turnovers other than steals (TOF) or defensive stops (STOP) would be like the Bush campaign saying, “I really don't want to calculate how many people will vote AGAINST Kerry, only how many will vote FOR Bush," or the Obama team deciding during their incredible Microtargeting of Virginia four years later that they really didn’t want to know why young Virginians might vote AGAINST McCain.

We simply have to do our best to calculate defense to have a whole picture of a player's overall statistical value to a team.

Here is what we can calculate from the 22% of individual defensive stats we do keep track of:

An average team blocks 9.2% of opponent’s 2-pointers takes away 2% of their points – double their blocks and they take away 4%. An average team steals the ball 9% of the time which takes away 9% of the opposition points. Double it and they take away 18% of the points. And finally a team that grabs the average 67.3% of opponents’ missed shots takes away 11% of their opponents points, so an individual player who gets one-fifth of that (13.5%) takes away just over 2% while he is on the floor. Here is the breakdown:

Adjust to ave. 1 pt. per trip

Pts+-

kenpom

Convert from Pomeroy

BLK

-2

9.2%

0.217

STL

-9

9.4%

0.957

REB (Defensive only)

-11

67.3%

0.163

No individual stat for 30 STOPS or TOF

-30

48 trips scored 100 points

52

To understand the Pts+-, consider that the AVERAGE possession results in just over 1.0 points. In the 100 possession in the first table, the team allowed points on 48 of them, but it was a total of 100 points scored (9 treys, 26 other field goals, and 19 free throws on the 13 times they finished with a made free throw). Since you expect 1 point allowed per trip, if you instead give up a 3-point shot then you are penalized for a+2 (3 is 2 points more than average), while a regular field goal is +1, etc., but a defensive stop is -1 since they didn’t get their one-point average that trip.

I’ve put in a converter so that you can multiply the Blk% in Pomeroy by .217 to determine what % of points a player is erasing from the opponents score with his blocked shots, steals and defensive rebounds.

However, that still leaves us way short of a Defensive Efficiency Rating (DRtg) that would match the precise Offensive Efficiency Rating (ORat) developed by Dean Oliver in 2003 and incorporated into www.kenpom.com that same year.

Introducing TeamDef72%

While we lack the INDIVIDUAL defensive stats we need, we do have them at the team level. Basically starting with Pomeroy’s Team Defensive Rating, which is the number of Points Allowed per possession, and taking out the influence of the teams above or below average defensive rebounding, steals and blocks, and we are left with one number that sums up a team’s Stops, Points Allowed and Turnovers Force. Here are the best and worst:

What this figure does is to give every player on a team one-fifth of the Stops, Points Allowed and Turnovers Forced while he is on the court, which gives him this figure. We see that when we add that all together, even after adjusting for competition as built into Pomeroy’s defensive rating, MU was below average for a BCS school in all the things you do on defense that aren’t recorded.

With 72% of their rating taken care of, we can now add back in each player’s blocks, steals and defensive rebounds.

Once I ran the numbers (and I really have no idea where everyone will finish until I sort by Value), Jared Sullinger calculated as the best returning defensive player in the country. To walk through his numbers:

1. Ohio State’s “TeamDef72%” stat for all the things we don’t measure was a 96.1, meaning they were the 6th best team at all the defensive things you do for which we do not have a stat. We start by giving Sullinger that number as one of the five Buckeyes on the court making that happen.2. Sullinger just missed being the top defensive rebounder in any BCS conference, and his 26.3% of rebounds grabbed after a miss can be multiplied by the factor on the table (.163)to convert this Pomeroy figure to save us some calculations in knowing his rebounding lower's opponents' scoring by 4.3%, so his defensive rating is lowered to 91.8.3. He is not a great shotblocker, only rejecting 2% of opponents two pointers, which when multiplied by the .217 on the converter tells us his shotblocking lowers opponents' scoring by 0.4% for a total for 91.4.4. Finally, he does get some steals, enough to erase another 1.9% of opponents scoring. For steals, we use the Stl% that actually appears on Pomeroy's page.5. This gives Sullinger an 89.5 Defensive Efficiency Rating, the flip side of the ORtg developed by Oliver in 2003. Not as precise as the ORtg because we are dividing up a lot of the team activity equally, but the same basis.6. You may notice that Bernard James of Florida State was actually a more effective defender when on the floor, with a DRtg of 86.3, meaning teams are only likely to score 86.3 points per trip per possession with him on the floor. Just ask Notre Dame about James after he played 21 minutes despite almost passing out sick, blocking 3 shots and grabbing 9 rebounds in that game to help to hold ND to 31% shooting from the floor. However, like with Kemba Walker in the blog on offense, it was more valuable for Sullinger to be on the floor for 78.9% of the minutes than for James to play 52.7% of the minutes.7. Once we multiply by minutes played, we see that over the course of the season Sullinger reduced the number of points scored by Ohio State opponents by -7.3%, the best total in the country, while James was just a little behind at -6.6% in 3rd place.

As will come as no surprise to all of us who watched last year, Marquette did not have the same kind of production on defense as they did on offense. Jae Crowder calculates as easily the best returning offensive and defensive player for Marquette, but he is the 24th best in the country on offense while the 160th best defensive player of the 2500+ returning.

The whole list to come, but here is how the Top 25 nationally calculate, followed by Marquette's returning players.

Def Rank ret

Fname

Lname

Feet

Inches

Team

Team Def Rating

DefR erase

Blk erase

Stl erase

DRtg

%Min

Value Sub BCS

Value Sub Low D1

1

Jared

Sullinger

6

9

Ohio St.

96.1

4.3

0.4

1.9

89.5

78.9

-7.3

2

John

Henson

6

10

North Carolina

95.9

4.1

2.5

1.1

88.1

66.6

-7.1

3

Bernard

James

6

10

Florida St.

94.4

3.0

2.9

2.2

86.3

52.7

-6.6

4

Tony

Mitchell

6

6

Alabama

97.1

2.6

0.8

3.0

90.7

78.3

-6.3

5

Mason

Plumlee

6

10

Duke

96.2

3.9

1.3

1.9

89.1

64.1

-6.2

6

Terrence

Jones

6

8

Kentucky

97.8

3.7

1.3

2.0

90.9

78.4

-6.2

7

Aaron

Craft

6

2

Ohio St.

96.1

1.6

0.1

3.9

90.5

73.8

-6.1

8

JaMychal

Green

6

8

Alabama

97.1

2.9

1.8

3.0

89.5

63.6

-5.9

9

Peyton

Siva

5

11

Louisville

96.1

1.8

0.2

3.9

90.2

68.4

-5.9

10

Deividas

Dulkys

6

5

Florida St.

94.4

1.4

0.5

2.8

89.8

60.7

-5.5

11

Michael

Snaer

6

5

Florida St.

94.4

1.3

0.2

1.7

91.2

71.2

-5.4

12

Tyler

Zeller

7

0

North Carolina

95.9

2.5

0.9

1.3

91.1

70.1

-5.4

13

Samme

Givens

6

5

Drexel

97.9

4.3

0.7

1.3

91.6

73.9

-5.3

14

Draymond

Green

6

6

Michigan St.

100.2

3.9

0.9

3.3

92.1

74.4

-5.0

15

Brady

Jardine

6

7

Utah St.

96.4

4.2

1.2

1.4

89.6

53.2

-4.9

16

William

Buford

6

5

Ohio St.

96.1

1.9

0.2

1.4

92.6

78.6

-4.9

17

Harrison

Barnes

6

8

North Carolina

95.9

2.1

0.3

1.3

92.1

73.2

-4.9

18

Trevor

Releford

6

1

Alabama

97.1

1.6

0.1

3.0

92.5

77.7

-4.9

19

Herb

Pope

6

8

Seton Hall

97.4

3.3

1.0

1.6

91.4

65.1

-4.8

20

Chase

Tapley

6

2

San Diego St.

95.2

1.3

0.2

3.0

90.7

58.2

-4.7

21

Dexter

Strickland

6

3

North Carolina

95.9

1.3

0.0

2.4

92.2

67.3

-4.5

22

Yancy

Gates

6

9

Cincinnati

98.5

3.4

1.1

1.7

92.2

67.2

-4.4

23

Chris

Smith

6

2

Louisville

96.1

2.2

0.1

1.9

91.9

63.8

-4.4

24

Brockeith

Pane

6

1

Utah St.

96.4

1.7

0.1

1.9

92.7

70.1

-4.3

25

Alex

Oriakhi

6

9

Connecticut

97.8

3.0

1.2

0.8

92.8

71.4

-4.3

160

Jae

Crowder

6

6

Marquette

103.0

3.0

0.7

2.6

96.6

68.8

-1.5

320

Chris

Otule

6

11

Marquette

103.0

2.2

1.9

1.0

97.9

43.8

-0.4

345

Vander

Blue

6

4

Marquette

103.0

1.9

0.3

2.6

98.2

47.3

-0.3

428

Darius

Johnson-Odom

6

2

Marquette

103.0

1.6

0.2

1.5

99.7

74.7

-6.7

495

Jamil

Wilson

6

7

Marquette (2010 Oregon)

106.8

2.4

0.7

1.5

102.1

78.4

-5.2

584

Junior

Cadougan

6

1

Marquette

103.0

1.4

0.1

1.5

100.0

47.9

-4.2

1003

Davante

Gardner

6

8

Marquette

103.0

2.4

0.4

0.8

99.4

19.9

-1.8

1943

David

Singleton

6

3

Marquette (2010 High Point)

116.7

2.0

0.2

3.6

110.9

78.4

NOTE FROM TOP TABLE: At first glance, the shooting percentages seem to be way too high when looking at this table, however that is because it only shows plays on which the possession ends. While there are more made shots than misses here, the fact is DURING some possessions teams may have shot 3 or 4 times and kept grabbing offensive rebounds, etc., so there are many more FGM than just those that end a possession with a defensive rebound.